import numpy as np
import matplotlib as mpl


def cohen_d(x, y):
    """
    Calculates the effect size Cohens d' between the two data x and y
    :param x: data array 1
    :param y: data array 2
    :return: float, d value of the effect size
    """
    nx = len(x)
    ny = len(y)
    dof = nx + ny - 2
    d = np.abs((np.mean(x) - np.mean(y)) / (((nx - 1) / dof) * (np.std(x) ** 2) + ((ny - 1) / dof) * (np.std(y) ** 2)))
    return d


def significance_bar(ax, p, d, x0, x1, y, **kwargs):
    """
    A horizontal bar with asterisks indicating significance level.

    Plot a horizontal bar from x0 to x1 at height y
    for indicating significance. On top of the bar plot
    asterisks according to the significance value p are drawn.
    If p > 0.05 nothing is plotted.
    p<0.001: '***', p<0.01: '**', p<0.05: '*'.

    Note: call this function AFTER ylim has been set!

    Parameters
    ----------
    ax: matplotlib axes
        Axes to which the inset is added.
    p: float
        Significance level.
    x0: float
        x-coordinate of starting point of significance bar in data units.
    x1: float
        x-coordinate of ending point of significance bar in data units.
    y: float
        y-coordinate of significance bar in data units.
    kwargs: key-word arguments
        Passed on to `ax.text()` used to print the asterisks.
    """
    if d is not None:
        if d > 0.0:
            ps = '%.2f' %(d)
        else:
            return

    elif p is not None:

        # set label:
        if p < 0.001:
            ps = '***'
        elif p < 0.01:
            ps = '**'
        elif p < 0.05:
            ps = '*'
        else:
            return
    # ax dimensions:
    pixely = np.abs(np.diff(ax.get_window_extent().get_points()[:, 1]))[0]
    unity = np.abs(np.diff(ax.get_ylim()))[0]
    dyu = unity / pixely
    fs = mpl.rcParams['font.size']
    if 'fontsize' in kwargs and isinstance(kwargs['fontsize'], (float, int)):
        fs = kwargs['fontsize']
    dy = 0.3 * fs * dyu
    lw = 1.0
    lh = ax.plot([x0, x0, x1, x1], [y - dy, y, y, y - dy], color='black', lw=lw,
                 solid_capstyle='butt', solid_joinstyle='miter', clip_on=False)
    # get y position of line in figure pixel coordinates:
    ly = np.array(lh[0].get_window_extent(ax.get_figure().canvas.get_renderer()))[1, 1]
    th = ax.text(0.5 * (x0 + x1), y, ps, ha='center', va='bottom', **kwargs)
    ty = np.array(th.get_window_extent(ax.get_figure().canvas.get_renderer()))[0, 1]
    dty = ly + 5 * lw - 0.4 * fs - ty
    th.set_position((0.5 * (x0 + x1), y + dty * dyu))